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Estimating reference intervals from an IPD meta-analysis using quantile regression.

Ziren Jiang¹, Haitao Chu^1,2, Zhen Wang³

¹Division of Biostatistics and Health Data Science, University of Minnesota, 2221 University Ave. SE., Ste. 200, Minneapolis, MN, 55414, USA.

BMC Medical Research Methodology

|October 27, 2024

Summary

This summary is machine-generated.

Quantile regression with individual participant data meta-analysis offers a non-parametric approach to establish precise reference intervals. This method enhances accuracy by avoiding distributional assumptions and enabling personalized intervals.

Keywords:

Bootstrap Individual participant data Meta-analysis Quantile regression Reference interval

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Area of Science:

Biostatistics
Medical Informatics
Epidemiology

Background:

Reference intervals are crucial in medical practice for interpreting patient data against healthy population norms.
Meta-analysis of multiple studies can yield more robust reference intervals than single studies.
Existing meta-analysis methods for reference intervals often rely on aggregate data and restrictive distributional assumptions.

Purpose of the Study:

To introduce quantile regression as a non-parametric method for estimating reference intervals from individual participant data (IPD) meta-analysis.
To enable the estimation of personalized reference intervals using patient-level covariates.
To address limitations of aggregate data and parametric assumptions in current meta-analysis approaches for reference intervals.

Main Methods:

Utilized quantile regression on individual participant data (IPD) within a fixed-effects meta-analysis model.
Employed non-parametric bootstrap methods to estimate reference intervals and account for within-study correlation.
Compared various bootstrap strategies through simulation studies to identify optimal approaches.

Main Results:

Quantile regression provides a flexible, non-parametric method for estimating reference intervals from IPD meta-analysis.
Simulation studies identified an optimal bootstrap strategy for estimating the uncertainty of reference intervals.
The recommended method involves fixing studies and randomly sampling subjects with replacement within each study under a fixed-effects model.

Conclusions:

Quantile regression is a powerful tool for estimating reference intervals from IPD meta-analysis, overcoming limitations of traditional methods.
The study provides an optimal bootstrap strategy for robust uncertainty estimation.
Demonstrated application using liver stiffness measurements in children highlights the utility for clinical diagnostic tests lacking established reference ranges.